{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Jupyter\n"
     ]
    }
   ],
   "source": [
    "print('Hello Jupyter')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D   E\n",
       "a   0   1   2   3   4\n",
       "b   5   6   7   8   9\n",
       "c  10  11  12  13  14\n",
       "d  15  16  17  18  19"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "# 创建一个多维数组\n",
    "data=pd.DataFrame(np.arange(20).reshape(4,5),index=list('abcd'),columns=list('ABCDE'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%lsmagic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Software\\\\python3\\\\study'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Software\\\\python3\\\\study'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = %pwd\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B   C   D   E\n",
      "a   0   1   2   3   4\n",
      "b   5   6   7   8   9\n",
      "c  10  11  12  13  14\n",
      "d  15  16  17  18  19\n"
     ]
    }
   ],
   "source": [
    "%run test.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%timeit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.7 µs ± 8.19 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "strings = ['foo', 'bazzle', 'quxix', 'python'] * 100\n",
    "%timeit [x for x in strings if x[:3] == 'foo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1e+03 µs\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D   E\n",
       "a   0   1   2   3   4\n",
       "b   5   6   7   8   9\n",
       "c  10  11  12  13  14\n",
       "d  15  16  17  18  19"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "data=pd.DataFrame(np.arange(20).reshape(4,5),index=list('abcd'),columns=list('ABCDE'))\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: TkAgg\n"
     ]
    }
   ],
   "source": [
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x147eb518>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(np.random.randn(60).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
